基于SAR图像匹配结果可信度评价的INS/SAR自适应Kalman滤波算法

doi:10.7527/S1000-6893.2019.22850

电子电气工程与控制

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基于SAR图像匹配结果可信度评价的INS/SAR自适应Kalman滤波算法

赵耀¹, 熊智¹, 田世伟^1,2, 刘建业¹, 崔雨晨¹

1. 南京航空航天大学自动化学院, 南京 211106;
2. 陆军工程大学通信工程学院, 南京 210007

收稿日期:2018-12-13 修回日期:2019-02-12 出版日期:2019-08-15 发布日期:2019-04-11
通讯作者: 熊智 E-mail:xiongzhi@nuaa.edu.cn
基金资助:
国家自然科学基金（61673208，61601511，61703208，61873125，61533008，61533009）；装备预研项目（30102080101）；江苏省自然科学基金（BK20181291，BK20170815，BK20170767）；中央高校基本科研业务费专项资金（NP2018108，NZ2019007）；江苏高校优势学科建设工程资助项目；江苏省"物联网与控制技术"重点实验室基金

INS/SAR adaptive Kalman filtering algorithm based on credibility evaluation of SAR image matching results

ZHAO Yao¹, XIONG Zhi¹, TIAN Shiwei^1,2, LIU Jianye¹, CUI Yuchen¹

1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. College of Communications Engineering, Army Engineering University, Nanjing 210007, China

Received:2018-12-13 Revised:2019-02-12 Online:2019-08-15 Published:2019-04-11
Supported by:
National Natural Science Foundation of China (61673208, 61601511, 61703208, 61873125, 61533008, 61533009); Advanced Research Project of the Equipment Development(30102080101); Natural Science Fund of Jiangsu Province (BK20181291,BK20170815, BK20170767); the Fundamental Research Funds for the Central Universities(NP2018108,NZ2019007); Foundation of Jiangsu Key Laboratory "Internet of Things and Control Technologies" & the Priority Academic Program Development of Jiangsu Higher Education Institutions

摘要/Abstract

摘要： 在惯性导航系统（INS）/合成孔径雷达（SAR）组合导航系统中，SAR图像易受斑点噪声的影响，图像匹配的精度对整个导航系统精度的影响十分明显，能够准确地分析SAR图像匹配过程中的误差特性，利用有效的图像匹配信息辅助INS进行组合定位尤为重要。针对上述问题，在加权Hausdorff距离匹配算法的基础上，对影响SAR图像匹配精度的因素进行了分析，提出了一种基于模糊推理的匹配结果可信度评价准则，经过可信度筛选，将有效的匹配信息与INS进行组合；对合理范围内的匹配误差变化引起量测噪声统计特性发生变化，进而导致Kalman滤波精度下降的问题，研究采用改进的Sage-Husa自适应滤波算法对量测噪声方差阵进行动态调整，使其更加接近系统的当前状态。搭建仿真验证平台对所提算法进行了验证，结果表明，该算法能够在合理的匹配误差范围内，有效地筛选出可信的图像匹配结果，相比常规Kalman滤波算法，显著地提升了INS/SAR组合导航系统水平方向的定位精度。

关键词: SAR图像匹配, 可信度评价准则, 模糊推理, 组合导航, 自适应滤波

Abstract: In Inertial Navigation System (INS)/Synthetic Aperture Radar (SAR) integrated navigation system, SAR image is susceptible to speckle noise. The accuracy of image matching has significant impact on the accuracy of the whole navigation system. It is especially important to accurately analyze the error characteristics in the SAR image matching process and to exploit the effective image matching information to assist the INS for integrated positioning. In response to the problems above, based on the weighted Hausdorff distance matching algorithm, the factors affecting the matching accuracy of SAR images are analyzed. Meanwhile, a set of criteria for credibility evaluation of matching results based on fuzzy reasoning is proposed, integrating the effective matching information with INS after credibility screening. Moreover, the variation of the matching error within a reasonable range will cause the statistical characteristics of the measurement noise to change, leading to the degradation of Kalman filtering accuracy. To address this problem, the improved Sage-Husa adaptive filtering method is adopted to dynamically adjust the measurement noise variance matrix closer to the current state of the system. The simulation verification platform is established to verify the proposed algorithm. The results indicate that the algorithm can effectively screen out the reliable image matching results within a reasonable range of matching error. Compared with the conventional Kalman filtering algorithm, the positioning accuracy of the INS/SAR integrated navigation system in the horizontal direction is significantly improved.

Key words: SAR image matching, credibility evaluation criteria, fuzzy reasoning, integrated navigation, adaptive filtering

中图分类号:

V243

赵耀, 熊智, 田世伟, 刘建业, 崔雨晨. 基于SAR图像匹配结果可信度评价的INS/SAR自适应Kalman滤波算法[J]. 航空学报, 2019, 40(8): 322850-322850.

ZHAO Yao, XIONG Zhi, TIAN Shiwei, LIU Jianye, CUI Yuchen. INS/SAR adaptive Kalman filtering algorithm based on credibility evaluation of SAR image matching results[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(8): 322850-322850.

参考文献 25

[1]	BAO X Q, BONTEMPS A, GRONDEL S, et al. Design and fabrication of insect-inspired composite wings for MAV application using MEMS technology[J]. Journal of Micromechanics & Microengineering, 2011, 21(12):1316-1317.
[2]	ZHAO H, XIONG Z, SHI L J, et al. A robust filtering algorithm for integrated navigation system of aerospace vehicle in launch inertial coordinate[J]. Aerospace Science and Technology, 2016, 58:629-640.
[3]	XING L, XIONG Z, LIU J Y, et al. Offline calibration for MEMS gyroscope G-sensitivity error coefficients based on the newton iteration and least square methods[J]. Journal of Navigation, 2018, 71(2):352-370.
[4]	刘中杰, 曹云峰,庄丽葵,等. 基于控制线方法的机载SAR和可见光图像匹配应用研究[J]. 航空学报, 2013, 34(9):2194-2201. LIU Z J, CAO Y F, ZHUANG L K, et al. Applied research on airborne SAR and optical image registraction based on control line method[J]. Acta Aeronautica et Astronautica Sinica, 2013, 34(9):2194-2201(in Chinese).
[5]	熊智, 冷雪飞,刘建业. 基于北斗双星定位辅助的SAR/INS组合导航系统研究[J]. 宇航学报, 2007, 28(1):88-93. XIONG Z, LENG X F, LIU J Y. Research of SAR/INS integrated navigation system aided by BeiDou double star position[J]. Journal of Astronautics, 2007, 28(1):88-93(in Chinese).
[6]	VḠ-UR S, ARIKAN O, CAFER G A. SAR image reconstruction by expectation maximization based matching pursuit[J]. Digital Signal Processing, 2015, 37(1):75-84.
[7]	XI R B, WANG Z M, XIE M H, et al. A non-local vectorial total variational model for multichannel SAR image speckle suppression[J]. Chinese Journal of Aeronautics, 2015, 28(3):770-779.
[8]	LI D P. A novel method for multi-angle SAR image matching[J]. Chinese Journal of Aeronautics, 2015, 28(1):240-249.
[9]	陈天泽, 李燕. 一种高性能SAR图像边缘点特征匹配方法[J]. 自动化学报, 2013, 39(12):2051-2063. CHEN T Z, LI Y.A high performance edge point feature match method of SAR images[J]. Acta Automatica Sinica, 2013, 39(12):2051-2063(in Chinese).
[10]	许允喜, 陈方. 基于CenSurE特征的SAR/INS组合导航景象匹配算法[J]. 控制与决策, 2011,26(8):1175-1180, 1186. XU Y X, CHEN F.Scene matching algorithm based on CenSurE for SAR/INS integrated navigation system[J]. Control and Decision, 2011, 26(8):1175-1180, 1186(in Chinese).
[11]	王峰, 尤红建,傅兴玉,等. 应用于多源SAR图像匹配的级联SIFT算法[J]. 电子学报, 2016, 44(3):548-554. WANG F, YOU H J, FU X Y, et al. Cascade SIFTmatching method for multi-source SAR images[J]. Acta Electronica Sinica, 2016, 44(3):548-554(in Chinese).
[12]	于秋则, 程辉,柳健,等. 基于改进Hausdorff测度和遗传算法的SAR图像与光学图像匹配[J]. 宇航学报, 2006(1):130-134. YU Q Z, CHENG H, LIU J, et al. Matching SAR image to optical image using modified Hausdorff distance and genetic algorithms[J]. Journal of Astronautics, 2006(1):130-134(in Chinese).
[13]	阮小丽, 陈庆虎,邱益鸣,等. 基于不变因子的SIFT误匹配点剔除及图像检索[J]. 红外技术, 2015, 37(7):560-565. RUAN X L, CHEN Q H, QIU Y M,et al. Excluding SIFT mismatching points based on the invariant factors and image retrieval[J]. Infrared Technology, 2015, 37(7):560-565(in Chinese).
[14]	刘佳, 傅卫平,王雯,等. 基于改进SIFT算法的图像匹配[J]. 仪器仪表学报, 2013, 34(5):1107-1112. LIU J, FU W P, WANG W, et al. Image matching based on improved SIFT algorithm[J]. Chinese Journal of Scientific Instrument, 2013, 34(5):1107-1112(in Chinese).
[15]	BATUR A, TURSUN G, MAMUT M, et al. Uyghur printed document image retrieval based on SIFT features[J]. Procedia Computer Science, 2017, 107:737-742.
[16]	冷雪飞, 刘建业,熊智,等. 加权Hausdorff距离算法在SAR/INS景象匹配中的应用[J]. 控制与决策, 2006, 21(1):42-45. LENG X F, LIU J Y, XIONG Z, et al. Application ofweighted Hausdorff distance algorithm in SAR/INS scene matching[J]. Control and Decision, 2006, 21(1):42-45(in Chinese).
[17]	曲圣杰, 潘泉,赵春晖,等. 景象匹配辅助导航系统匹配置信度评估方法[J]. 中国惯性技术学报, 2011, 19(6):713-719. QU S J, PAN Q, ZHAO C H, et al. Confidence evaluation method for scene matching aided navigation system[J]. Journal of Chinese Inertial Technology, 2011, 19(6):713-719(in Chinese).
[18]	陈建祥, 桑农. 一种实用的边缘强度互相关匹配可信度分析方法[J]. 宇航学报, 2005, 26(6):722-725. CHEN J X, SANG N. A practical matching confidence analysis technique based on neural networks for normalized edge magnitude cross correlation[J]. Journal of Astronautics, 2005, 26(6):722-725(in Chinese).
[19]	朱占龙, 单友东,杨翼,等. 基于新息正交性自适应滤波的惯性/地磁组合导航方法[J]. 中国惯性技术学报, 2015, 23(1):66-70. ZHU Z L, SHAN Y D, YANG Y, et al.INS/GNS integrated method based on innovation orthogonality adaptive Kalman filter[J]. Journal of Chinese Inertial Technology, 2015, 23(1):66-70(in Chinese).
[20]	薛丽, 高社生,胡高歌. 自适应Sage-Husa粒子滤波及其在组合导航中的应用[J]. 中国惯性技术学报, 2013, 21(1):84-88. XUE L, GAO S S, HU G G.Adaptive Sage-Husa particle filtering and its application in integrated navigation[J]. Journal of Chinese Inertial Technology, 2013, 21(1):84-88(in Chinese).
[21]	李刚, 赵德阳,解瑞春,等. 基于改进的Sage-Husa自适应扩展卡尔曼滤波的车辆状态估计[J]. 汽车工程, 2015, 37(12):1426-1432. LI G, ZHAO D Y, XIE R C, et al.Vehicle state estimation based on improved Sage-Husa adaptive extended Kalman filtering[J]. Automotive Engineering, 2015, 37(12):1426-1432(in Chinese).
[22]	XIA L L, ZHAO Y, MA W J, et al. MIMUs/GPS information fusion:Normal cloud model based fuzzy adaptive filtering[C]//2018 Chinese Control And Decision Conference (CCDC). Piscataway, NJ:IEEE Press, 2018:4076-4081.
[23]	RODRIGUEZ R M, BEDREGAL B, BUSTINCE H, et al. A position and perspective analysis of hesitant fuzzy sets on information fusion in decision making. Towards high quality progress[J]. Information Fusion, 2016, 29:89-97.
[24]	张倩. SAR图像质量评估及其目标识别应用[D]. 合肥:中国科学技术大学, 2011:23-28. ZHANG Q.Quality assessment and target recognition in SAR images[D]. Hefei:University of Science and Technology of China, 2011:23-28(in Chinese).
[25]	刘建业, 曾庆化,赵伟,等. 导航系统原理与应用[M]. 西安:西北工业大学出版社, 2010:339-346. LIU J Y, ZENG Q H, ZHAO W, et al.Theory and application of navigation system[M]. Xi'an:Northwestern Polytechnical University Press, 2010:339-346(in Chinese).

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基于SAR图像匹配结果可信度评价的INS/SAR自适应Kalman滤波算法

INS/SAR adaptive Kalman filtering algorithm based on credibility evaluation of SAR image matching results

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